Combining traffic-shaping methods with congestion control variants...

In this paper, we describe a new method, called RWTM (Receive Window Tuning Method) that shapes HTTP adaptive streams. It employs the flow control in the gateway to improve the quality of experience (QoE) of users. Our use case is when two HTTP Adaptive streaming clients are competing for bandwidth in the same home network. Results show that our proposed method considerably improves the QoE; it improves the video stability, the fidelity to optimal video quality level selection and the convergence speed to the optimal video quality level.

HTTP Adaptive Streaming (HAS) is a streaming video technique commonly employed over best-effort networks. However, it is characterized by some issues that harm its quality of experience (QoE) in cases of daily use. The main use case of the present investigation involves HAS clients competing for bandwidth inside the same home network. Based on related work, we found that one of the most convenient solutions for this use case is to define a bandwidth manager, on the gateway side, that divides the available home bandwidth between HAS clients. Two main methods have previously been proposed to shape the HAS streams in accordance with the bandwidth manager's direction and are referred to as gateway-based shaping methods: a highly renowned method, Hierarchical Token Bucket Method (HTBM), that uses the hierarchical token bucket queuing discipline, and another method, Receive Window Tuning Method (RWTM), that employs TCP flow control by handling only acknowledgment TCP packets. In this paper, we compare these two shaping methods. Results indicate that RWTM improves the QoE better than HTBM and does not add queuing delay. Results were validated through experimentation and objective QoE analytical criteria.

HTTP Adaptive Streaming (HAS) is a widely used video streaming technology that suffers from a degradation of user's Quality of Experience (QoE) and network's Quality of Service (QoS) when many HAS players are sharing the same bottleneck link and competing for bandwidth. The two major factors of this degradation are: the large OFF period of HAS, which causes false bandwidth estimations, and the TCP congestion control, which is not suitable for HAS given that it does not consider the different video encoding bitrates of HAS. This paper proposes a HAS-based TCP congestion control, TcpHas, that minimizes the impact of the two aforementioned issues. It does this by using traffic shaping on the server. Simulations indicate that TcpHas improves both QoE, mainly by reducing instability, and QoS, mainly by reducing queuing delay and packet drop rate.